Voice traffic prediction is significant for network deployment optimization thus to improve the network efficiency. The real entropy based theorectical bound and corresponding prediction models have demonstrated their success in mobility prediction. In this paper, the real entropy based predictability analysis and prediction models are introduced into voice traffic prediction. For this adoption, the traffic quantification methods is proposed and discussed. Based on the real world voice traffic data, the prediction accuracy of N-order Markov models, diffusion based model and MF model are presented, among which, 25-order Markov models performs best and approach close to the maximum predictability. This work demonstrates that, the real entropy can also predict voice traffic well which broaden the understanding on the real entropy based prediction theory.

中文翻译：

---

Real Entropy 还可以预测无线网络用户的日常语音流量

语音流量预测对于网络部署优化从而提高网络效率具有重要意义。基于真实熵的理论界和相应的预测模型已经证明了它们在移动性预测中的成功。本文将基于真实熵的可预测性分析和预测模型引入语音流量预测中。为此，提出并讨论了交通量化方法。基于真实世界的语音流量数据，给出了N阶马尔可夫模型、基于扩散模型和MF模型的预测精度，其中25阶马尔可夫模型表现最好，接近于最大可预测性。这项工作表明，